With increasing use of pump therapy for Type 1 diabetic patients, young and old alike, the importance of controlling the infusion device such as external infusion pumps is evident. Indeed, presently available external infusion devices typically include an input mechanism such as buttons through which the patient may program and control the infusion device. Such infusion devices also typically include a user interface such as a display which is configured to display information relevant to the patient's infusion progress, status of the various components of the infusion device, as well as other programmable information such as patient specific basal profiles.
The external infusion devices are typically connected to an infusion set which includes a cannula that is placed transcutaneously through the skin of the patient to infuse a select dosage of insulin based on the infusion device's programmed basal rates or any other infusion rates as prescribed by the patient's doctor. Generally, the patient is able to control the pump to administer additional doses of insulin during the course of wearing and operating the infusion device such as for, administering a carbohydrate bolus prior to a meal. Certain infusion devices include food database that has associated therewith, an amount of carbohydrate, so that the patient may better estimate the level of insulin dosage needed for, for example, calculating a bolus amount.
However, in general, most estimation or calculation of a bolus amount for administration, or a determination of a suitable basal profile, for that matter, are educated estimates based on the patient's physiology as determined by the patient's doctor, or an estimate performed by the patient. Moreover, the infusion devices do not generally include enhancement features that would better assist the diabetic patients to control and/or manage the glucose levels.
In view of the foregoing, it would be desirable to have an approach to provide methods and system for data processing associated with a patient's monitored analyte levels for providing semi automatic or automatic recommendation based on the processed data such as, for example, therapy profile, glucose level pattern recognition, and the like.